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Setup

Create a new directory with these three files (requirements.txt, main.py, README.md)
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python main.py
Update main() to run the example prompt chains

Note

Hi, everyone. I've been putting in a lot of work on this over the last few ~~weeks~~ months (sob) and i'm currently underemployed! If you'd like to hire me to do CMS-based work (i focus on Craft and ExpressionEngine but i do some WordPress work as well), please reach out! Alternatively, if you'd like to chip in toward bills & groceries, that's a big help right now!

Updates (Most Recent First)

Reversing Raw Binary Firmware Files in Ghidra

This brief tutorial will show you how to go about analyzing a raw binary firmware image in Ghidra.

Prep work in Binwalk

I was recently interested in reversing some older Cisco IOS images. Those images come in the form of a single binary blob, without any sort of ELF, Mach-o, or PE header to describe the binary.

While I am using Cisco IOS Images in this example, the same process should apply to other Raw Binary Firmware Images.

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

General rules

Follow standard conventions.
Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
Boy scout rule. Leave the campground cleaner than you found it.
Always find root cause. Always look for the root cause of a problem.

Design rules

Reverse Engineering on macOS

Some notes, tools, and techniques for reverse engineering macOS binaries.

	You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

	## Core Principles

	1. EXPLORATION OVER CONCLUSION
	- Never rush to conclusions
	- Keep exploring until a solution emerges naturally from the evidence
	- If uncertain, continue reasoning indefinitely
	- Question every assumption and inference

	#!/usr/bin/python
	# -- coding: utf-8 --
	texts =[u"वाराणसी", u"भौगोलिक", u"उपदर्शन"]
	signs = [
	u'\u0902', u'\u0903', u'\u093e', u'\u093f', u'\u0940', u'\u0941',
	u'\u0942', u'\u0943', u'\u0944', u'\u0946', u'\u0947', u'\u0948',
	u'\u094a', u'\u094b', u'\u094c', u'\u094d']
	limiters = ['.','\"','\'','`','!',';',',','?']

	virama = u'\u094d'

	sudo yum update

	# Installing ZSH
	sudo yum -y install zsh

	# Check ZSH has been installed
	zsh --version

	# Install "util-linux-user" because "chsh" is not available by default
	# See https://superuser.com/a/1389273/599050

	"""
	This model integrates the MoE concept within a Transformer architecture. Each token's
	representation is processed by a subset of experts, determined by the gating mechanism.
	This architecture allows for efficient and specialized handling of different aspects of the
	data, aiming for the adaptability and efficiency noted in the Mixtral 8x7B model's design
	philosophy. The model activates only a fraction of the available experts for each token,
	significantly reducing the computational resources needed compared to activating all experts
	for all tokens.
	"""

	fpsMeter() {
	let prevTime = Date.now(),
	frames = 0;

	requestAnimationFrame(function loop() {
	const time = Date.now();
	frames++;
	if (time > prevTime + 1000) {
	let fps = Math.round( ( frames * 1000 ) / ( time - prevTime ) );
	prevTime = time;